Job Title: Supply Chain Data Analyst
Primary Job Location: Toronto, ON, Canada
Location Flexibility: Hybrid, Toronto based
Employment Type (Permanent/Contract/Part-time/Intern): Permanent, Full-Time
Hiring Timeline (Hiring month): Immediate
Reporting Line: Chief Scientist
Existing Vacancy: Yes
Application Deadline: Open until filled
Role OverviewYou will drive the data analytics capabilities of Workbench, FIQ's warehouse design platform, by automating the processing, cleaning and analysis of large datasets from warehouse and logistics operations. You will bring a data-centric and product-oriented mindset to Workbench, working hand-in-hand with our domain experts and software engineering team. As a result, you will help turn data-driven insights into product features that automate time-consuming and error-prone tasks which, today, are mostly performed manually in spreadsheets.
What You'll Do- Process, clean and analyze large customer supply chain datasets, for example a year of 3PL order data, and extract relevant insights that can be turned into valuable decisions.
- Identify patterns and similarities across heterogeneous datasets and consolidate and automate our data analytics capabilities across our customer base.
- Identify which analyses matter most and how it should be presented, be it a metric, a graph, or something else. While you will not be expected to build production-grade UI components, you will develop data visualization demos and prototypes.
- Help get Workbench to the point where users answer most of their own data questions inside the platform instead of in a spreadsheet, in a fraction of the time it currently takes them.
- Lean hard on AI to move fast through the execution and spend your judgment on what is worth automating. We are a small but mighty team, and that is our DNA.
The Honest Version of the Hard Parts- We work with real, large, and messy data. You will spend real time organizing, cleaning and reconciling datasets before you get to the interesting part, and you have to be honest about what you trust.
- The manual analysis is the means, not the job. If you just want to churn through datasets forever, this is the wrong seat. The whole point is to see the repeated pattern and get it built into the product.
- The goal is not to repeat a manual process repeatedly: it is to build comprehensive capabilities that can automate that process as broadly and as efficiently as possible.
- You sit between the data and the engineers. You have to translate what you see into something a dev team can build, and hold the thread on what capabilities will bring the most value to our users.
- Domain knowledge matters more than proficiency in specific data processing tools. If you do not know supply chain or logistics, you will feel it, because the analysis only means something in context.
Where This GoesThink chessboard, not ladder. You will start by owning the data-analysis capability inside Workbench, and there is real room to grow into data-product ownership and into shaping how the whole platform makes sense of supply chain data. You will work directly with the Chief Scientist. Tell us what you want to build and we will help you get there.
How We'll Interview You- Intro conversation: a short call to swap context and make sure the basics line up.
- Conversation with the Chief Scientist: how you think about data and supply chain, and whether we can make each other better.
- Technical: a live working session on a real, messy supply chain dataset. Clean it, find the patterns that matter, and tell us what you would automate and how you would present it.
- Final: discussion with the Workbench team.
What You Need to Have- Supply chain or logistics domain experience. We would rather have someone who knows the operation and can pick up the data tools than a strong analyst who has never seen supply chain data.
- Fluency with at least one tabular-data tool (e.g. pandas, Polars, Arrow). You have worked with real, messy data end to end, not just tidy classroom sets.
- The business sense to see the repeated pattern and turn it into a clear ask for an engineering team, and the communication to present it well.
- Enough visualization judgment to say what to show and how, even though you will not build the front end.
- A Bachelor's in Data Science, Statistics, Industrial Engineering, or a related quantitative field. We care more about how you think and your domain than your exact number of years.
- Nice to have: Warehouse, fulfillment, or order-management operations exposure.
- Nice to have: Experience working shoulder to shoulder with a product or engineering team.
- Nice to have: Tableau or Power BI, or GPU dataframe tooling.
Why You Will Love Working HereReal ownership on a team small enough that your judgment shapes what gets built, working directly with the Chief Scientist on a product that is genuinely new. You will build the capability, not inherit a dashboard and babysit it. We move fast, we automate the busywork, and we back people who take initiative.
Compensation and BenefitsFIQ posts good-faith pay ranges. The base salary range below reflects experience, location, and internal equity.
Canada (Ontario)Base salary range: 85,000K - 110,000K per year.
Health and Wellness- Comprehensive extended health and dental coverage for you and your family
- Employee wellness programs where applicable
Time Off- Competitive paid time off, sick leave, and public holidays
- Flexible leave policies that respect local labour standards
Retirement and Financial Security- CPP contributions and a group retirement savings plan with employer contributions
- Employee stock options (ESOP), where applicable
Professional Growth- Dedicated learning and development budget, with support for skills, leadership, and career progression
Flexible Work- Remote and hybrid work options, flexible hours aligned to role and client needs
Additional Perks- Equipment and workstation allowances, internet and business travel reimbursements, team events and offsites
Work Authorization Applicants must be legally authorized to work in Canada.
